4.6 Linear Regression and Median Fit Lines
Wednesday, November 9, 2016
Students write equations of bestfit lines using linear regression, and write equations of median fit lines.
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Homework.
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4.1 Vocab.
Slope Intercept Form An equation in the form y = mx + b
Constant Functions Written in slope intercept form as y = 0x + b, or y = b
4.3 Vocab.
Point Slope Form An equation in the
form y y1 = m(xx1)
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BestFit Line A very precise line of fit.
Linear Regression A complex algorithm that calculates the bestfit line.
Correlation Coefficient This number, r, tells you if the correlation is positive or negative and how closely the best fit line models the data. The number, r, is always between 1 and 1.
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Correlation Coefficient, r
If r is greater than zero and less than or equal to 1. We have a positive correlation. The closer it is to 1, the stronger the positive association is.
If r is greater than or equal to 1 and less than 0. We have a negative correlation. The closer we get to 1, the stronger the negative correlation is.
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Regression Line: y = .23206x + 456.032 Correlation Coefficient: .875515
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Regression Line:
Correlation Coefficient:
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Regression Line:
Correlation Coefficient:
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Regression Line
Median Fit Line
Correlation Coefficient
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Regression Line
Median Fit Line
Correlation Coefficient
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